Shennan Aibel Weiss1, Brent Berry2, Inna Chervoneva3, Zachary Waldman4, Jonathan Guba4, Mark Bower2, Michal Kucewicz2, Benjamin Brinkmann2, Vaclav Kremen2, Fatemeh Khadjevand2, Yogatheesan Varatharajah2, Hari Guragain2, Ashwini Sharan5, Chengyuan Wu5, Richard Staba6, Jerome Engel6, Michael Sperling7, Gregory Worrell2. 1. Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA. Electronic address: Shennan.Weiss@jefferson.edu. 2. Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA. 3. Dept. of Pharmacology & Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. 4. Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA. 5. Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, USA. 6. Dept. of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA. 7. Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA.
Abstract
OBJECTIVE: To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80-200 Hz) and fast ripples (200-600 Hz) in intra-operative electrocorticography (ECoG) recordings. METHODS: Sixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. RESULTS: Visual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632-0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50-100%]. Including false positive detections reduced the PPV to 64.2 [57.8-69.83%]. CONCLUSIONS: Applying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SIGNIFICANCE: Semi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.
OBJECTIVE: To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80-200 Hz) and fast ripples (200-600 Hz) in intra-operative electrocorticography (ECoG) recordings. METHODS: Sixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. RESULTS: Visual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632-0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50-100%]. Including false positive detections reduced the PPV to 64.2 [57.8-69.83%]. CONCLUSIONS: Applying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SIGNIFICANCE: Semi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.
Authors: Shennan A Weiss; Zachary Waldman; Federico Raimondo; Diego Slezak; Mustafa Donmez; Gregory Worrell; Anatol Bragin; Jerome Engel; Richard Staba; Michael Sperling Journal: Biomark Med Date: 2019-05-02 Impact factor: 2.851
Authors: Cesar Santana-Gomez; Pedro Andrade; Matthew R Hudson; Tomi Paananen; Robert Ciszek; Gregory Smith; Idrish Ali; Brian K Rundle; Xavier Ekolle Ndode-Ekane; Pablo M Casillas-Espinosa; Riikka Immonen; Noora Puhakka; Nigel Jones; Rhys D Brady; Piero Perucca; Sandy R Shultz; Asla Pitkänen; Terence J O'Brien; Richard Staba Journal: Epilepsy Res Date: 2019-03-15 Impact factor: 3.045
Authors: Vasileios Dimakopoulos; Jean Gotman; William Stacey; Nicolás von Ellenrieder; Julia Jacobs; Christos Papadelis; Jan Cimbalnik; Gregory Worrell; Michael R Sperling; Maike Zijlmans; Lucas Imbach; Birgit Frauscher; Johannes Sarnthein Journal: Brain Commun Date: 2022-06-09
Authors: Shennan A Weiss; Tomas Pastore; Iren Orosz; Daniel Rubinstein; Richard Gorniak; Zachary Waldman; Itzhak Fried; Chengyuan Wu; Ashwini Sharan; Diego Slezak; Gregory Worrell; Jerome Engel; Michael R Sperling; Richard J Staba Journal: Brain Commun Date: 2022-04-20
Authors: Shennan A Weiss; Inkyung Song; Mei Leng; Tomás Pastore; Diego Slezak; Zachary Waldman; Iren Orosz; Richard Gorniak; Mustafa Donmez; Ashwini Sharan; Chengyuan Wu; Itzhak Fried; Michael R Sperling; Anatol Bragin; Jerome Engel; Yuval Nir; Richard Staba Journal: Front Neurol Date: 2020-03-24 Impact factor: 4.003